| Challenge: | Existing studies have failed to explore co-attentive multi-modal modeling for visual and text reasoning. |
| Approach: | They propose to use image and multi-modal Transformers to reconstruct fMRI brain activity . they use two popular datasets to study visual and text reasoning . |
| Outcome: | The proposed model outperforms existing models on two popular datasets . the results raise the question whether visual processing is affected implicitly by linguistic processing . |
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| Challenge: | Existing literature has focused on pretrainer-based text-driven brain encoding models . however, few studies have explored the efficacy of task-specific learning of Transformers . |
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| Challenge: | Recent studies suggest that transformer-based vision-language models capture the multimodality of concept processing in the human brain. |
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Encoding and Decoding Language in the Brain with Language Models (2026.eacl-tutorials)
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| Challenge: | This tutorial introduces brain-language model alignment and recent advances in brain-informed fine-tuning and brain-based fine-caching with language models. |
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Yuko Nakagi, Takuya Matsuyama, Naoko Koide-Majima, Hiroto Yamaguchi, Rieko Kubo, Shinji Nishimoto, Yu Takagi
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Language Reconstruction with Brain Predictive Coding from fMRI Data (2026.acl-long)
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| Challenge: | Existing studies have shown that the perception of speech can be decoded from brain signals and subsequently reconstructed as continuous language. |
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| Challenge: | a number of approaches to crossmodal representation have been used, but transformer architecture has taken over the recurrent neural networks in natural language processing tasks. |
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Computational Linguistics for Brain Encoding and Decoding: Principles, Practices and Beyond (2024.acl-tutorials)
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| Challenge: | This tutorial will explore the potential of computational linguistics to help understand brain language processing. |
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Mapping Brains with Language Models: A Survey (2023.findings-acl)
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| Challenge: | accumulated evidence for brain and language model activations remains ambiguous, but correlations with model size and quality provide grounds for cautious optimism. |
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Linking artificial and human neural representations of language (D19-1)
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| Challenge: | a pre-trained BERT architecture is used to fine-tune sentence encoding models on a variety of natural language understanding (NLU) tasks. |
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Implicit Representations of Meaning in Neural Language Models (2021.acl-long)
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| Challenge: | Neural language models (NLMs) encode lexical relations and syntactic structure, but their effectiveness is still unclear. |
| Approach: | They propose to use text as a model to model entities and situations as they evolve throughout a discourse. |
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